Get 1:1 mentorship, coaching, and guidance from Tier-1 architects
AI Systems Engineering
Educational partners
Want a top-tier role? Here's how you get there:
Build real-world projects. Ship production-grade systems.
Practice technical and HR interviews so you can excel at the real thing
Henrietta, coach
Ji-ho
Is this advanced program for you?
“You should have two years of software development experience, or similar experience in QA, DevOps, or another role with production systems.”
“You should have two years of software development experience, or similar experience in QA, DevOps, or another role with production systems.”
Dr. Gönen ErenCurriculum AuthorWho you areYour skill gapsHow you'll bridge them
Who you are
Backend & Fullstack Engineers
You create APIs and business logic, but need skills in distributed systems and AI.
Your skill gaps
System decompositionMonolith scalingData consistency
How you'll bridge them
- Build full-scale production system across 5 progressive projects
- Defend your architecture 1-on-1 with Senior Engineers
Who you are
Infrastructure, DevOps & SRE
You manage pipelines and uptime; complex AI workloads need new performance approaches
Your skill gaps
Cloud & K8s for AIAdvanced observabilitySecurity review
How you'll bridge them
- Target only your gaps with modular theory
- Get weekly 1-on-1 mentorship to debug your code or real work cases
Who you are
Data Engineers & Tech Pros
You have a STEM background but lack experience in distributed systems design
Your skill gaps
Architecture & API designCloud/DevOpsSystem Design interviews
How you'll bridge them
- Fill technical gaps with optional theory tracks
- Practice mock technical and HR interviews with debriefs after every round
If you are switching careers into tech for the first time, explore our other programs
Career switching programsWork with a senior engineer from day one
Day 1
Meet the mentor you'll work with one-on-one for the entire program.
During the program
Mentors assess your architecture, examining how issues were resolved and identifying failures.
Capstone
Defend your design decisions to a staff-level engineer, the same way you would to a hiring manager.
Mohamed CherifInstructor
Principal / Staff Software Engineer
Dr. Gönen ErenCurriculum Author
Founder & CTO; most recently .NET APIs on AWS
20 years building distributed IoT backends on Azure, high-throughput telemetry pipelines, .NET APIs, and workflow engines on AWS. Ph.D. in Computer Science, master's from ETH Zürich.
Ahmed AbouzeidInstructor
Sr. Manager, Software Development
15+ years shipping distributed systems, platform infrastructure, and GenAI-powered experiences at massive scale across AWS and Amazon.
Support at every stage
Live engineer-led defense
Projects end with a 1-on-1 to present your architecture and defend your code
Personalized lesson plan
Skip what you already know and focus only on your specific gaps with guided support
1-on-1 mentorship
Meet weekly for 30 minutes 1-on-1 with senior engineers to review code or discuss job challenges
Mock interview practice with people
Up to 3 technical interview with engineers and 3 behavioral rounds with a coach, plus a debrief after
Your complete AI-enabled advanced program
Mock interviews & debriefs
Practice technical & HR interviews to hone your performance and fix blind spots.
Lifelong access
Come back any time to review curriculum updates and use your alumni career coaching sessions.
Support all the way through the offer
From take-home tests
to salary negotiation support
Optimized job applications
We'll help you create job search materials that get attention.
A global network built for your career leap
Join a network of leading engineers: your peers, instructors, and 7,500 TripleTen alumni across the US and Latin America.
Work with engineers from TripleTen's partner, Nebius Group.
Powered by 800+ engineers, Nebius offers full-stack AI infrastructure services for industry leaders including Nvidia, Meta, & Microsoft.
Access the hidden job market and build your referral network while you study.
300+
Daily opportunities on our job board.
Get a chance to mentor beginner tech students. Gain leadership experience.
Cara
Hey! I'm working on the e-commerce practice set, and I'm a bit stuck on filtering and aggregating…
🤔1
Demetrus
Great question! This is where SQL really shines compared to spreadsheet tools.
❤️1Build a production-ready system that showcases your expertise
Projects first. Theory when you need it.
Focus on project work. Fill knowledge gaps with 250+ optional hours of lessons.
Five skills. One workflow.
Combine observability, RAG, cloud queues, security, and AI agents in one workflow.
Real-world engineering tradeoffs.
Learn to make decisions that mirror production engineering environments.
Apply system design principles to maintain a stable public API contract across schema migrations and underlying database refactoring.
Tools used:
RESTgRPCPostgreSQLRedis
Theory behind it
API and service architectureVersioning and deprecation strategyRelational data modelingRead replicationCache invalidationContract testing
Starting state
A running PostgreSQL cluster, a partial API spec, and active production traffic on v1.
Engineering decisions
Read replica placement, caching boundaries, and versioning/deprecation strategy for breaking changes.
Evaluation criteria
Zero disruption to existing v1 clients, backed by passing contract and integration tests.
Rely on practical problem solving to build reliable deployment pipelines and maintain observable, production-grade Kubernetes infrastructure under failure conditions.
Tools used:
ReactStorybookTailwind CSSGraphQLPostgreSQL
Theory behind it
Containers and orchestrationInfrastructure as codeCI/CD pipelinesSLOs and error budgets
Starting state
A production Kubernetes cluster, live telemetry dashboards, and an active simulated incident.
Engineering decisions
Deployment strategy, automated rollback paths, and alert thresholds/paging rules.
Evaluation criteria
Mean time to recovery (MTTR), rollback stability, and whether telemetry provides clear root-cause visibility.
Design an idempotent message-processing pipeline and secure service mesh capable of surviving network chaos and state replay.
Tools used:
KafkaOAuth 2.0VaultThreat modeling
Theory behind it
Event-driven architectureExactly-once and idempotency patternsChaos engineeringZero-trust authenticationSecrets managementAutomated security scanning
Starting state
A Kafka pipeline, an incomplete threat model, and an active chaos-engineering harness.
Engineering decisions
Idempotency key strategy, zero-trust network boundaries (mTLS/least-privilege IAM), and CI/CD security gate enforcement.
Evaluation criteria
State consistency under event replay, pipeline fault tolerance during simulated infrastructure failures, and compliance with the threat model.
Master context engineering and cost optimization to build production agentic AI features backed by automated evals.
Tools used:
RAGAgentic workflowsEvalsLLM gateway
Theory behind it
LLM gateway designRouting and fallback chainsRAG architecture and chunking strategyAgent orchestrationEvaluation harnesses
Starting state
Raw platform data for RAG, a strict token budget, and an empty evaluation framework.
Engineering decisions
Model routing and fallback logic, RAG chunking strategy, and CI/CD eval gating thresholds.
Evaluation criteria
Cost per request, quantitative retrieval metrics (e.g., precision/recall), and evals that block failing builds.
Synthesize prior projects into a unified production architecture and defend your design decisions.
Tools used:
C4 modelADRsScaling reviewRisk register
Theory behind it
Architecture documentation with the C4 modelDecision recordsTradeoff analysisTechnical communication
Starting state
Your integrated end-to-end agentic AI platform and complete system design documentation.
Engineering decisions
System trade-off justifications, technical debt prioritization, and a 10× traffic scaling strategy.
Evaluation criteria
A 60-minute technical review and architecture defense with an independent Staff Engineer.
Ready to level up?Let's talk.
Book a call with an admissions advisor to review your skills audit, get your questions answered, and pick the right program.
Repeatedly recognized and highly rated
Highly rated 6 years in a row
4.7/5
Rigorous curriculum, with academic partner:
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6 in 10 employees see AI as a coworker.
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Why it pays to be polite to AI — by Lee Hafner (Jun 4, 2026).
Flexible payment options
Upfront payment
Pay in a single installment, or up to 4 payments. You can withdraw with a 100% refund in your first 2 weeks.
upfront payment of
$12,600 in total$12,600
Tuition financing
Apply for financing with one of our partners, and learn if you are eligible in minutes. Soft credit check req'd.
Monthly payments starting at
$17,950 in total$350
TripleTen installments
$1,000 deposit, instantly approved. No credit check req'd, 0% interest! 6, 12, or 24-month plans available.
monthly payments from
$20,000 in total$1,206
Our financial partners for tuition financing:
Upfront payment
Pay in a single installment, or up to 4 payments. You can withdraw with a 100% refund in your first 2 weeks.
upfront payment of
$12,600 in total$12,600
Can be split into 2 or 4 payments
Tuition financing
Apply for financing with one of our partners, and learn if you are eligible in minutes. Soft credit check req'd.
Our financial partners for tuition financing:
Monthly payments starting at
$17,950 in total$350
TripleTen installments
$1,000 deposit, instantly approved. No credit check req'd, 0% interest! 6, 12, or 24-month plans available.
monthly payments from
$20,000 in total$1,206
Frequently asked questions
Who is the AI Systems Engineering program for?
The program is for engineers with 2+ years of hands-on production experience who want to move into AI systems roles:
Backend and full-stack engineers who build APIs and business logic and need distributed systems and AI skills
DevOps, SRE, and infrastructure engineers who run pipelines and uptime and need to support AI workloads
Data and QA engineers with production experience who want to learn system and API design
Admission starts with a 3-minute skills audit. If you're new to tech, the programs for complete beginners are a better fit.
Do I need to quit my job to take the program?
No. You study 10–20 hours a week, live sessions run on evenings and weekends, and the program length adjusts to your pace.
How long is the program and how is it structured?
The program takes 22 weeks at 10–20 hours a week, or about 8 months at 10 hours a week. It includes 330 hours of project work across five projects that build on each other, plus 250+ hours of optional lessons: you skip the topics you already know. Each project ends with a 1:1 pre-review and a 60-minute live review with a working engineer.
What will I learn in the AI Systems Engineering curriculum?
You learn to take a system from feature-level code to production-grade AI infrastructure:
System design: service architecture with REST, gRPC, and GraphQL, data architecture, caching, and safe migrations
Cloud and operations: Docker, Kubernetes, Terraform/OpenTofu, CI/CD, Argo CD, and event-driven systems with Kafka
Observability and security: Prometheus, Grafana, OpenTelemetry, zero-trust security, and compliance
Production AI: RAG with pgvector, model serving with vLLM, agents with LangGraph, LLM evaluation, and prompt-injection defense
What projects will I build in the program?
You build one production-style platform across five projects. You diagnose it for 10x growth, split it into services with a polyglot data tier, run it on Kubernetes through a failure lab, harden it with zero-trust security and a grounded AI feature, and finish with an autonomous AI system you present to a staff-level engineer. A sample project, Autonomous Incident Defense, is on TripleTen's GitHub.
Does the program include real work experience?
Yes, in a production setting: you work in a production-style codebase, submit pull requests, and pass automated tests, a reviewer, and an instructor's sign-off before each live review. Optional partner-company projects and hackathons are also available but not guaranteed.
What kind of support and guidance can I expect during my studies?
From day one, you work with a mentor and meet for 30 minutes 1:1 every week to review your code or a problem from your job. Instructors are senior engineers with backgrounds at AMD, Microsoft, Amazon, and AWS.
You also get group sessions on evenings and weekends, a learning coach who keeps you on plan, and career coaching that starts during the program and continues for up to 3 months after graduation. You keep lifelong access to curriculum updates.
What portfolio will I be able to show employers after I finish?
You finish with one deployed production AI system on GitHub, built across five projects and reviewed live by working engineers, plus written feedback from every review. In system design interviews, you can walk interviewers through the architecture decisions you made and explained in those reviews.
Can I move from DevOps or SRE into AI platform engineering with this program?
Yes. DevOps, SRE, and infrastructure engineers already run the pipelines AI systems depend on, and the program adds Kubernetes for AI workloads, advanced observability, security review, and production LLM serving. Graduates target roles such as AI Platform Engineer and AI Infrastructure Engineer.
Does the program prepare me for system design and LLM interview rounds?
Yes. Each live project review works like a system design round, and career coaching adds up to three technical mock interviews with engineers and up to three behavioral mocks with a coach, with a debrief after each one. You practice explaining your own LLM integration, retrieval, and evaluation choices, the topics LLM interview rounds ask about.